Thermostat Classification Method and System

• US 20140074300A1
• Filed: 03/13/2013
• Published: 03/13/2014
• Est. Priority Date: 09/07/2012
• Status: Active Grant

• Current Assignee: Opower, Inc. (Oracle Corporation)
• Original Assignee: Opower, Inc. (Oracle Corporation)
• Inventors: Curtis, Richard Tyler, Shilts, Erik, Ben-Meshulam, Jonathan Z.
• Granted Patent: US 9,547,316 B2
• Time in Patent Office: Days
• US Class Current: 700/276
• CPC Class Codes: G05D 23/01 without auxiliary power
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

The disclosure provides a computer-implemented method and system of reducing commodity usage by providing tailored consumer information to the consumer. The method utilizes neural network and machine learning techniques to calculate and cluster statistical data to classify the premises for desired observable condition, including the presence of a programmed thermostat. A score is determined that corresponds to at least one of: (i) a present state of an observable condition, (ii) a non-present state of the observable condition, and (iii) a degree of a condition of the observable condition, to provide tailored consumer information associated to the consumer'"'"'s usage of the commodity.

Claims

1. A computer-implemented method of reducing commodity usage by providing tailored consumer information to a consumer, the computer-implemented method comprising:

receiving energy usage data of a consumer associated to a premises, the premises having a controller to regulate temperature in a controlled space within the premises, the energy usage data being associated, in part, to the regulation of the controller;

determining, via a processor, a status of whether the controller adjusts the temperature according to a time-of-day dependent setting, the status-determination being based on the received energy usage data; and

providing tailored consumer information based on the determined status, the information being associated to the consumer'"'"'s usage of the commodity.

2. The computer-implemented method of claim 1, wherein determining the status of whether the controller adjusts the temperature according to a time-of-day dependent setting includes:

comparing a first statistical feature derived from the received energy usage data to a second statistical feature derived from at least one representative pattern of a premises with a controller that adjusts the temperature according to a time-of-day dependent setting.

3. The computer-implemented method of claim 1, wherein providing tailored consumer information includes:

outputting a report to the consumer at the premises based on the determined status, the report including, at least one of;

a message indicating at least one of an estimated reduction in usage of the commodity and an estimated reduction in cost associated with the estimated reduction in usage if the programmed controller is employed at the premises; and

an indication of the presence of the malfunctioned HVAC at the premises.

4. The computer-implemented method of claim 1, wherein the energy usage data is retrieved from at least one of an advanced meter infrastructure (AMI) meter and a home display system located at the consumer'"'"'s premises.

5. The computer-implemented method of claim 1, wherein determining the status includes:

calculating, from the energy usage data, statistical data related to at least one of seasonality, trend, decomposed data, kurtosis, skewness, serial correlation, non-linear autocorrelation, long range dependence, and chaos; and

assigning the calculated statistical data to a representative self-organized map, the map having at least one cluster corresponding to a premises having at least one of a programmed thermostat and an unprogrammed thermostat.

6. The computer-implemented method of claim 5, wherein the representative self-organized map is determined from at least one of k-means, hierarchical clustering, Kohonen self-organizing maps, and other neural networks.

7. The computer-implemented method of claim 5, wherein the representative self-organizing map includes at least one of a Kohonen supervised self-organizing maps and a Kohonen unsupervised self-organizing map.

8. The computer-implemented method of claim 5, wherein the representative self-organizing map corresponds to a neighborhood associated to the premises.

9. The computer-implemented method of claim 1, wherein determining the status includes:

calculating, from the energy usage data, statistical data related to at least one of seasonality, trend, decomposed data, kurtosis, skewness, serial correlation, non-linear autocorrelation, long range dependence, and chaos;

applying the statistical data to a regression model, the regression model having a plurality of model coefficients corresponding to a predictor variable of whether the controller adjusts the temperature according to a time-of-day dependent setting, the status-determination being based on the received energy usage data; and

scoring the premises using the coefficients to determine whether the controller adjusts the temperature according to a time-of-day dependent setting.

10. The computer-implemented method of claim 9, wherein the regression model includes minimizing a sum of squared error between a predicted and an actual value.

11. The computer-implemented method of claim 9 further comprising:

filtering, prior to applying the calculated statistical data to the regression model, a portion of the statistical data that is insufficiently predictive based upon radical change in the model coefficient.

12. The computer-implemented method of claim 11, wherein filtering is performed by at least one of a ridge estimator, a Lasso estimator, and a least angle regression estimator.

13. The computer-implemented method of claim 9, wherein the predictor variable includes at least one parameter corresponding to seasonality, intercept, trend, decomposed data, kurtosis, skewness, serial correlation, non-linear autocorrelation, long range dependence, chaos, and sensitivity to heating and cooling.

14. The computer-implemented method of claim 1, wherein determining the status includes:

calculating, from the energy usage data, statistical data related to at least one of seasonality, trend, decomposed data, kurtosis, skewness, serial correlation, non-linear autocorrelation, long range dependence, and chaos; and

assigning the calculated statistical data to a representative self-organized map, a portion of the map having a cluster having an associated regression model;

applying the statistical data to the associated regression model regression model based on the assignment, the regression model having a plurality of model coefficients corresponding to a predictor variable of whether the controller adjusts the temperature according to a time-of-day dependent setting, the status-determination being based on the received energy usage data; and

scoring the energy usage data using the coefficients to determine the status of whether the controller adjusts the temperature according to a time-of-day dependent setting.

15. The computer-implemented method of claim 1, wherein determining the status includes:

calculating a ratio of usage in the evening and usage in the morning;

comparing the calculated ratio to a user-specific threshold, wherein the comparison provides the status of whether the controller adjusts the temperature according to a time-of-day dependent setting.

16. A computer-implemented method of remotely ascertaining presence of a programmed thermostat controlling an environmental conditioner of premises, the method comprising:

fetching energy usage data obtained by a device located at the premises;

at a location remote from the premises, performing a statistical analysis of the energy usage data, including analyzing the energy usage data for an indication of a repeated temporal variation therein;

if the indication of the repeated temporal variation is found, storing information indicating a programmed thermostat controlling an environmental conditioner of the premises is present, otherwise storing information indicating no such programmed thermostat is present.

17. A method according to claim 16, further comprising, if the stored information indicates no such programmed thermostat is present:

generating a message recommending usage of a programmed thermostat; and

sending the message to a consumer associated with the premises.

18. A method according to claim 16, further comprising, if the stored information indicates a programmed thermostat is present:

further analyzing the usage data for an indication of excessive energy usage by the environmental conditioner;

if the indication of excessive energy usage is found, generating a message recommending a course of action for reducing energy usage; and

sending the message to a consumer associate with the premises.

19. A system comprising:

a memory configured to store energy usage data of a premises of a consumer, the premises having a controller to regulate temperature in a controlled space within the premises, the energy usage data being associated, in part, to the regulation of the controller;

a control program configured to

control the memory;

receive the energy usage data;

determine, via a processor, a status of whether the controller adjusts the temperature according to a time-of-day dependent setting, the status-determination being based on the received energy usage data; and

output the status.

20. The system of claim 19 further comprising:

a communication port configured to receive the energy usage data from an AMI meter and storing the energy usage data in the memory.

21. A computer program product, which is stored on a machine-readable medium, or computer data signal, embodied by an electromagnetic wave, comprising program code for carrying out a the method of reducing commodity usage by providing tailored consumer information to a consumer, the computer-program product comprising:

computer code to receive energy usage data of a consumer associated to a premises, the premises having a controller to regulate temperature in a controlled space within the premises, the energy usage data being associated, in part, to the regulation of the controller; and

computer code to determine, via a processor, a status of whether the controller adjusts the temperature according to a time-of-day dependent setting, the status-determination being based on the received energy usage data; and

computer code to output the status.

22. A computer-implemented method of reducing commodity usage by providing tailored consumer information to a consumer, the computer-implemented method comprising:

receiving usage data of a commodity by a consumer associated to a premises of the consumer;

calculating, from the usage data, statistical data related to at least one of seasonality, trend, decomposed data, kurtosis, skewness, serial correlation, non-linear autocorrelation, long range dependence, and chaos;

classifying the statistical data to generate classified result data by clustering the statistical data;

scoring the premises using the classified result data to determine a determined observable condition at the premises, the determined observable condition including at least one of;

(i) a present state of an observable condition, (ii) a non-present state of the observable condition, and (iii) a degree of a condition of the observable condition; and

providing tailored consumer information associated to the consumer'"'"'s usage of the commodity based on the determined observable condition.

23. The computer-implemented method of claim 22, wherein the observable condition includes, at least one of:

whether the premises has a programmed controller configured to control the usage of the commodity, such as a programmable thermostat;

whether the consumer is present;

the number of consumer present on the premises;

whether the premises has an electric water heater;

whether the premises has a malfunctioning heating, ventilation, and air-conditioning (HVAC) system; and

the degree of the malfunctioning heating, ventilation, and air-conditioning (HVAC) system.